International Journal of Computational Intelligence Systems

Volume 10, Issue 1, 2017, Pages 176 - 195

An Application of Dynamic Bayesian Networks to Condition Monitoring and Fault Prediction in a Sensored System: a Case Study

Authors
Javier Cózar1, javier.cozar@uclm.es, José M. Puerta1, jose.puerta@uclm.es, José A. Gámez1, jose.gamez@uclm.es
Received 8 June 2016, Accepted 24 September 2016, Available Online 1 January 2017.
DOI
10.2991/ijcis.2017.10.1.13How to use a DOI?
Keywords
Bayesian networks; anomaly detection; sensor networks; predictive maintenance; condition monitoring
Abstract

Bayesian networks have been widely used for classification problems. These models, structure of the network and/or its parameters (probability distributions), are usually built from a data set. Sometimes we do not have information about all the possible values of the class variable, e.g. data about a reactor failure in a nuclear power station. This problem is usually focused as an anomaly detection problem. Based on this idea, we have designed a decision support system tool of general purpose.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
10 - 1
Pages
176 - 195
Publication Date
2017/01/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.2017.10.1.13How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Javier Cózar
AU  - José M. Puerta
AU  - José A. Gámez
PY  - 2017
DA  - 2017/01/01
TI  - An Application of Dynamic Bayesian Networks to Condition Monitoring and Fault Prediction in a Sensored System: a Case Study
JO  - International Journal of Computational Intelligence Systems
SP  - 176
EP  - 195
VL  - 10
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2017.10.1.13
DO  - 10.2991/ijcis.2017.10.1.13
ID  - Cózar2017
ER  -